* Training using STARE data set and DRIVE data set (with manual segmentations by observer 1). Use this version for best results.

Run

The executable has only one argument, which is the image file to process.

This executable can be used for non-commercial purposes only.

Results

Four files are written for each run: 1) Likelihood Ratio Vesselness at each pixel of the input image (header+content), 2) Vessel matched filter computed at each pixel of the input image (header+content). Each of these outputs is in the Insight Toolkit RAW format, because they are floating point outputs. Extension .mhd specifies the file header and extension .raw is the image content. Do not linearly scale the output of the LRV filter before use (i.e. by stretching to <0, 255> interval of integer intensities) because it is a likelihood ratio and you will loose important responses (many would get rounded to zero).

If you work in MATLAB, you can use the following function to read the raw images: readRaw.m Example Usage:

Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matched-filter responses, confidence measures and vessel boundary measures. Matched filter responses are derived in scale-space to extract vessels of widely varying widths. A vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile. Vessel boundary measures and associated confidences are computed at potential vessel boundaries. Combined, these responses form a 6-dimensional measurement vector at each pixel. A training technique is used to develop a mapping of this vector to a likelihood ratio that measures the “vesselness” at each pixel. Results comparing this vesselness measure to matched filters alone and to measures based on the Hessian of intensities show substantial improvements both qualitatively and quantitatively. The Hessian can be used in place of the matched filter to obtain similar but less-substantial improvements or to steer the matched filter by preselecting kernel orientations. Finally, the new vesselness likelihood ratio is embedded into a vessel tracing framework, resulting in an efficient and effective vessel centerline extraction algorithm.